VLTA Volta Inc. Class A Common Stock

Outlook: Volta Inc. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating.
Dominant Strategy : Hold
Time series to forecast n: 13 Apr 2023 for (n+8 weeks)
Methodology : Modular Neural Network (Emotional Trigger/Responses Analysis)

Abstract

Volta Inc. Class A Common Stock prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and ElasticNet Regression^1,2,3,4 and it is concluded that the VLTA stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

Key Points

1. Buy, Sell and Hold Signals
2. What are the most successful trading algorithms?
3. Can we predict stock market using machine learning?

VLTA Target Price Prediction Modeling Methodology

We consider Volta Inc. Class A Common Stock Decision Process with Modular Neural Network (Emotional Trigger/Responses Analysis) where A is the set of discrete actions of VLTA stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^1,2,3,4

F(ElasticNet Regression)^5,6,7= $\begin{array}{cccc}{p}_{\mathrm{a}1}& p_{\mathrm{a}2}& \dots & p_{1n}\\ & ⋮\\ p_{j1}& p_{j2}& \dots & p_{jn}\\ & ⋮\\ p_{k1}& p_{k2}& \dots & p_{kn}\\ & ⋮\\ p_{n1}& p_{n2}& \dots & p_{nn}\end{array}$ X R(Modular Neural Network (Emotional Trigger/Responses Analysis)) X S(n):→ (n+8 weeks) $\sum _{i=1}^n\left(a_i\right)$

n:Time series to forecast

p:Price signals of VLTA stock

j:Nash equilibria (Neural Network)

k:Dominated move

a:Best response for target price

 

For further technical information as per how our model work we invite you to visit the article below: 

How do AC Investment Research machine learning (predictive) algorithms actually work?

VLTA Stock Forecast (Buy or Sell) for (n+8 weeks)

Sample Set: Neural Network
Stock/Index: VLTA Volta Inc. Class A Common Stock
Time series to forecast n: 13 Apr 2023 for (n+8 weeks)

According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

IFRS Reconciliation Adjustments for Volta Inc. Class A Common Stock

1. However, an entity is not required to separately recognise interest revenue or impairment gains or losses for a financial asset measured at fair value through profit or loss. Consequently, when an entity reclassifies a financial asset out of the fair value through profit or loss measurement category, the effective interest rate is determined on the basis of the fair value of the asset at the reclassification date. In addition, for the purposes of applying Section 5.5 to the financial asset from the reclassification date, the date of the reclassification is treated as the date of initial recognition.
2. Sales that occur for other reasons, such as sales made to manage credit concentration risk (without an increase in the assets' credit risk), may also be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows. In particular, such sales may be consistent with a business model whose objective is to hold financial assets in order to collect contractual cash flows if those sales are infrequent (even if significant in value) or insignificant in value both individually and in aggregate (even if frequent). If more than an infrequent number of such sales are made out of a portfolio and those sales are more than insignificant in value (either individually or in aggregate), the entity needs to assess whether and how such sales are consistent with an objective of collecting contractual cash flows. Whether a third party imposes the requirement to sell the financial assets, or that activity is at the entity's discretion, is not relevant to this assessment. An increase in the frequency or value of sales in a particular period is not necessarily inconsistent with an objective to hold financial assets in order to collect contractual cash flows, if an entity can explain the reasons for those sales and demonstrate why those sales do not reflect a change in the entity's business model. In addition, sales may be consistent with the objective of holding financial assets in order to collect contractual cash flows if the sales are made close to the maturity of the financial assets and the proceeds from the sales approximate the collection of the remaining contractual cash flows.
3. However, in some cases, the time value of money element may be modified (ie imperfect). That would be the case, for example, if a financial asset's interest rate is periodically reset but the frequency of that reset does not match the tenor of the interest rate (for example, the interest rate resets every month to a one-year rate) or if a financial asset's interest rate is periodically reset to an average of particular short- and long-term interest rates. In such cases, an entity must assess the modification to determine whether the contractual cash flows represent solely payments of principal and interest on the principal amount outstanding. In some circumstances, the entity may be able to make that determination by performing a qualitative assessment of the time value of money element whereas, in other circumstances, it may be necessary to perform a quantitative assessment.
4. When measuring hedge ineffectiveness, an entity shall consider the time value of money. Consequently, the entity determines the value of the hedged item on a present value basis and therefore the change in the value of the hedged item also includes the effect of the time value of money.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

Conclusions

Volta Inc. Class A Common Stock is assigned short-term Ba1 & long-term Ba1 estimated rating. Volta Inc. Class A Common Stock prediction model is evaluated with Modular Neural Network (Emotional Trigger/Responses Analysis) and ElasticNet Regression^1,2,3,4 and it is concluded that the VLTA stock is predictable in the short/long term. According to price forecasts for (n+8 weeks) period, the dominant strategy among neural network is: Hold

VLTA Volta Inc. Class A Common Stock Financial Analysis*

Rating	Short-Term	Long-Term Senior
Outlook*	Ba1	Ba1
Income Statement	Baa2	Baa2
Balance Sheet	B1	B2
Leverage Ratios	Ba1	B1
Cash Flow	Baa2	B3
Rates of Return and Profitability	Baa2	Caa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

Prediction Confidence Score

Trust metric by Neural Network: 77 out of 100 with 474 signals.

References

1. Candès EJ, Recht B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9:717
2. Athey S. 2017. Beyond prediction: using big data for policy problems. Science 355:483–85
3. Ruiz FJ, Athey S, Blei DM. 2017. SHOPPER: a probabilistic model of consumer choice with substitutes and complements. arXiv:1711.03560 [stat.ML]
4. Wooldridge JM. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press
5. Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 25, ed. Z Ghahramani, M Welling, C Cortes, ND Lawrence, KQ Weinberger, pp. 1097–105. San Diego, CA: Neural Inf. Process. Syst. Found.
6. Bengio Y, Schwenk H, Senécal JS, Morin F, Gauvain JL. 2006. Neural probabilistic language models. In Innovations in Machine Learning: Theory and Applications, ed. DE Holmes, pp. 137–86. Berlin: Springer
7. Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, et al. 2008. Top 10 algorithms in data mining. Knowl. Inform. Syst. 14:1–37

Frequently Asked Questions

Q: What is the prediction methodology for VLTA stock?
A: VLTA stock prediction methodology: We evaluate the prediction models Modular Neural Network (Emotional Trigger/Responses Analysis) and ElasticNet Regression
Q: Is VLTA stock a buy or sell?
A: The dominant strategy among neural network is to Hold VLTA Stock.
Q: Is Volta Inc. Class A Common Stock stock a good investment?
A: The consensus rating for Volta Inc. Class A Common Stock is Hold and is assigned short-term Ba1 & long-term Ba1 estimated rating.
Q: What is the consensus rating of VLTA stock?
A: The consensus rating for VLTA is Hold.
Q: What is the prediction period for VLTA stock?
A: The prediction period for VLTA is (n+8 weeks)

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